An independently validated nomogram for isocitrate dehydrogenase-wild-type glioblastoma patient survival.

BackgroundIn 2016, the World Health Organization reclassified the definition of glioblastoma (GBM), dividing these tumors into isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant GBM, where the vast majority of GBMs are IDH-wild-type. Nomograms are useful tools for individualized estimation of survival. This study aimed to develop and independently validate a nomogram for IDH-wild-type patients with newly diagnosed GBM.MethodsData were obtained from newly diagnosed GBM patients from the Ohio Brain Tumor Study (OBTS) and the University of California San Francisco (UCSF) for diagnosis years 2007-2017 with the following variables: age at diagnosis, sex, extent of resection, concurrent radiation/temozolomide (TMZ) status, Karnofsky Performance Status (KPS), O6-methylguanine-DNA methyltransferase (MGMT) methylation status, and IDH mutation status. Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using the OBTS data and independently validated using the UCSF data. Models were internally validated using 10-fold cross-validation and externally validated by plotting calibration curves.ResultsA final nomogram was validated for IDH-wild-type newly diagnosed GBM. Factors that increased the probability of survival included younger age at diagnosis, female sex, having gross total resection, having concurrent radiation/TMZ, having a high KPS, and having MGMT methylation.ConclusionsA nomogram that calculates individualized survival probabilities for IDH-wild-type patients with newly diagnosed GBM could be useful to physicians for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this nomogram is provided: https://gcioffi.shinyapps.io/Nomogram_For_IDH_Wildtype_GBM_H_Gittleman/

Prognostic risk stratification of gliomas using deep learning in digital pathology images.

BackgroundEvaluation of tumor-tissue images stained with hematoxylin and eosin (H&E) is pivotal in diagnosis, yet only a fraction of the rich phenotypic information is considered for clinical care. Here, we propose a survival deep learning (SDL) framework to extract this information to predict glioma survival.MethodsDigitized whole slide images were downloaded from The Cancer Genome Atlas (TCGA) for 766 diffuse glioma patients, including isocitrate dehydrogenase (IDH)-mutant/1p19q-codeleted oligodendroglioma, IDH-mutant/1p19q-intact astrocytoma, and IDH-wildtype astrocytoma/glioblastoma. Our SDL framework employs a residual convolutional neural network with a survival model to predict patient risk from H&E-stained whole-slide images. We used statistical sampling techniques and randomized the transformation of images to address challenges in learning from histology images. The SDL risk score was evaluated in traditional and recursive partitioning (RPA) survival models.ResultsThe SDL risk score demonstrated substantial univariate prognostic power (median concordance index of 0.79 [se: 0.01]). After adjusting for age and World Health Organization 2016 subtype, the SDL risk score was significantly associated with overall survival (OS; hazard ratio = 2.45; 95% CI: 2.01 to 3.00). Four distinct survival risk groups were characterized by RPA based on SDL risk score, IDH status, and age with markedly different median OS ranging from 1.03 years to 14.14 years.ConclusionsThe present study highlights the independent prognostic power of the SDL risk score for objective and accurate prediction of glioma outcomes. Further, we show that the RPA delineation of patient-specific risk scores and clinical prognostic factors can successfully demarcate the OS of glioma patients

Walking Speed Influences the Effects of Implicit Visual Feedback Distortion on Modulation of Gait Symmetry

The use of visual feedback in gait rehabilitation has been suggested to promote recovery of locomotor function by incorporating interactive visual components. Our prior work demonstrated that visual feedback distortion of changes in step length symmetry entails an implicit or unconscious adaptive process in the subjects’ spatial gait patterns. We investigated whether the effect of the implicit visual feedback distortion would persist at three different walking speeds (slow, self-preferred and fast speeds) and how different walking speeds would affect the amount of adaption. In the visual feedback distortion paradigm, visual vertical bars portraying subjects’ step lengths were distorted so that subjects perceived their step lengths to be asymmetric during testing. Measuring the adjustments in step length during the experiment showed that healthy subjects made spontaneous modulations away from actual symmetry in response to the implicit visual distortion, no matter the walking speed. In all walking scenarios, the effects of implicit distortion became more significant at higher distortion levels. In addition, the amount of adaptation induced by the visual distortion was significantly greater during walking at preferred or slow speed than at the fast speed. These findings indicate that although a link exists between supraspinal function through visual system and human locomotion, sensory feedback control for locomotion is speed-dependent. Ultimately, our results support the concept that implicit visual feedback can act as a dominant form of feedback in gait modulation, regardless of speed

DETECTION OF GLIOMA INFILTRATION AT THE TUMOR MARGIN USING QUANTITATIVE STIMULATED RAMAN SCATTERING MICROSCOPY

BACKGROUND: Differentiating neoplastic tumor tissues from normal brain at the infiltrative tumor margin remains challenging. Stimulated Raman scattering microscopy (SRM) is a rapid, label-free technique that provides microscopic imaging of unprocessed tissues. However, it has never been studied as a tool to enhance extent of resection. METHODS: In this single center study, patients with WHO II-IV gliomas undergoing surgical resection were included. Margin samples were taken with corresponding stereotactic coordinates. Each tumor margin sample was analyzed using (1) H&E, (2) SRM, and (3) immunohistochemical (IHC) stains for IDH1-R132H or p53. Specimens were imaged fresh with SRM, inked for orientation and placed in formalin for routine processing. Stained slides were scored by 3-neuro-pathologists using a semiquantitative 2-tiered scoring system for the presence or absence of residual tumor. SRM microscopy images were segmented into cellularity. The intraobserver measure of agreement between modalities for each pathologist was calculated using Cohen’s kappa. RESULTS: A total of 31 patients and 179 margin specimens were acquired. Postoperative MRI confirmed that 169 (94%) of glioma margin sample coordinates were indeed at the tumor margins (10 of 179 were not true margins). All 10 samples which were not true margins were identified as having residual tumor by SRM. IHC semiquantitative scoring of margin specimen confirmed 72 of 128 samples (56%) had residual tumor. Similarly, H&E-scoring confirmed 82 of 169 samples (49%) and 82 of 167 samples (49%) by SRM-scoring had residual tumor. Intraobserver agreements between all 3 modalities confirmed agreement: IHC-SRM was near perfect (K-0.84, CI-0.750-0.94); IHC-H&E substantial, (K-0.67 CI-0.54-0.80); SRM-H&E agreement substantial (K-0.72 CI-0.62-0.83). Margin samples with residual tumor demonstrated higher tissue cellularity when compared with samples without tumor (p< 0.0001). CONCLUSIONS: SRM allows for identification of residual microscopic disease at the infiltrative tumor margin and therefore may be a promising tool to enhance extent of resection

SURG-07. DETECTION OF GLIOMA INFILTRATION AT THE TUMOR MARGIN USING QUANTITATIVE STIMULATED RAMAN SCATTERING MICROSCOPY

Abstract BACKGROUND Differentiating neoplastic tumor tissues from normal brain at the infiltrative tumor margin remains challenging. Stimulated Raman scattering microscopy (SRM) is a rapid, label-free technique that provides microscopic imaging of unprocessed tissues. However, it has never been studied as a tool to enhance extent of resection. METHODS In this single center study, patients with WHO II-IV gliomas undergoing surgical resection were included. Margin samples were taken with corresponding stereotactic coordinates. Each tumor margin sample was analyzed using (1) H&amp;E, (2) SRM, and (3) immunohistochemical (IHC) stains for IDH1-R132H or p53. Specimens were imaged fresh with SRM, inked for orientation and placed in formalin for routine processing. Stained slides were scored by 3-neuro-pathologists using a semiquantitative 2-tiered scoring system for the presence or absence of residual tumor. SRM microscopy images were segmented into cellularity. The intraobserver measure of agreement between modalities for each pathologist was calculated using Cohen’s kappa. RESULTS A total of 31 patients and 179 margin specimens were acquired. Postoperative MRI confirmed that 169 (94%) of glioma margin sample coordinates were indeed at the tumor margins (10 of 179 were not true margins). All 10 samples which were not true margins were identified as having residual tumor by SRM. IHC semiquantitative scoring of margin specimen confirmed 72 of 128 samples (56%) had residual tumor. Similarly, H&amp;E-scoring confirmed 82 of 169 samples (49%) and 82 of 167 samples (49%) by SRM-scoring had residual tumor. Intraobserver agreements between all 3 modalities confirmed agreement: IHC-SRM was near perfect (K-0.84, CI-0.750-0.94); IHC-H&amp;E substantial, (K-0.67 CI-0.54-0.80); SRM-H&amp;E agreement substantial (K-0.72 CI-0.62-0.83). Margin samples with residual tumor demonstrated higher tissue cellularity when compared with samples without tumor (p&lt; 0.0001). CONCLUSIONS SRM allows for identification of residual microscopic disease at the infiltrative tumor margin and therefore may be a promising tool to enhance extent of resection

Detection of glioma infiltration at the tumor margin using quantitative stimulated Raman scattering histology

Abstract In the management of diffuse gliomas, the identification and removal of tumor at the infiltrative margin remains a central challenge. Prior work has demonstrated that fluorescence labeling tools and radiographic imaging are useful surgical adjuvants with macroscopic resolution. However, they lose sensitivity at the tumor margin and have limited clinical utility for lower grade histologies. Fiber-laser based stimulated Raman histology (SRH) is an optical imaging technique that provides microscopic tissue characterization of unprocessed tissues. It remains unknown whether SRH of tissues taken from the infiltrative glioma margin will identify microscopic residual disease. Here we acquired glioma margin specimens for SRH, histology, and tumor specific tissue characterization. Generalized linear mixed models were used to evaluate agreement. We find that SRH identified residual tumor in 82 of 167 margin specimens (49%), compared to IHC confirming residual tumor in 72 of 128 samples (56%), and H&E confirming residual tumor in 82 of 169 samples (49%). Intraobserver agreements between all 3 modalities were confirmed. These data demonstrate that SRH detects residual microscopic tumor at the infiltrative glioma margin and may be a promising tool to enhance extent of resection

Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging

BackgroundDiagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning.MethodsOur dataset consisted of 384 patients with newly diagnosed gliomas who underwent preoperative MRI with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models.ResultsThe best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI: [77.1, 100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5%-17.5%, and models that included diffusion-weighted imaging were 5%-8.8% more accurate than those that used only anatomical imaging.ConclusionTraining a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then 1p19q-codeletion. Including apparent diffusion coefficient (ADC), a surrogate marker of cellularity, more accurately captured differences between subgroups

Interactions of Age and Blood Immune Factors and Noninvasive Prediction of Glioma Survival.

BackgroundTumor-based classification of human glioma portends patient prognosis, but considerable unexplained survival variability remains. Host factors (eg, age) also strongly influence survival times, partly reflecting a compromised immune system. How blood epigenetic measures of immune characteristics and age augment molecular classifications in glioma survival has not been investigated. We assess the prognostic impact of immune cell fractions and epigenetic age in archived blood across glioma molecular subtypes for the first time.MethodsWe evaluated immune cell fractions and epigenetic age in archived blood from the University of California San Francisco Adult Glioma Study, which included a training set of 197 patients with IDH-wild type, 1p19q intact, TERT wild type (IDH/1p19q/TERT-WT) glioma, an evaluation set of 350 patients with other subtypes of glioma, and 454 patients without glioma.ResultsIDH/1p19q/TERT-WT patients had lower lymphocyte fractions (CD4+ T, CD8+ T, natural killer, and B cells) and higher neutrophil fractions than people without glioma. Recursive partitioning analysis delineated 4 statistically significantly different survival groups for patients with IDH/1p19q/TERT-WT based on an interaction between chronological age and 2 blood immune factors, CD4+ T cells, and neutrophils. Median overall survival ranged from 0.76 years (95% confidence interval = 0.55-0.99) for the worst survival group (n = 28) to 9.72 years (95% confidence interval = 6.18 to not available) for the best (n = 33). The recursive partitioning analysis also statistically significantly delineated 4 risk groups in patients with other glioma subtypes.ConclusionsThe delineation of different survival groups in the training and evaluation sets based on an interaction between chronological age and blood immune characteristics suggests that common host immune factors among different glioma types may affect survival. The ability of DNA methylation-based markers of immune status to capture diverse, clinically relevant information may facilitate noninvasive, personalized patient evaluation in the neuro-oncology clinic